title: Highly accurate prediction of food challenge outcome using routinely available clinical data.
abstract: BACKGROUND
  Serum specific IgE or skin prick tests are less useful at levels below accepted decision points.
  
  
  OBJECTIVE
  We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge.
  
  
  METHODS
  The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk.
  
  
  RESULTS
  Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively).
  
  
  CONCLUSIONS
  Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed.
authors.affiliation: Paediatrics and Child Health, University College Cork, Cork, Ireland. a.dunngalvin@ucc.ie
authors.forename: Audrey
authors.initials: A
authors.lastname: DunnGalvin
authors.forename: Deirdre
authors.initials: D
authors.lastname: Daly
authors.forename: Claire
authors.initials: C
authors.lastname: Cullinane
authors.forename: Emily
authors.initials: E
authors.lastname: Stenke
authors.forename: Diane
authors.initials: D
authors.lastname: Keeton
authors.forename: Mich
authors.initials: M
authors.lastname: Erlewyn-Lajeunesse
authors.forename: Graham C
authors.initials: GC
authors.lastname: Roberts
authors.forename: Jane
authors.initials: J
authors.lastname: Lucas
authors.forename: Jonathan O'B
authors.initials: JO
authors.lastname: Hourihane
mesh_terms: D000465:Algorithms; D000818:Animals; D010367:Arachis / Q000276:immunology; D002648:Child; D005260:Female; D005512:Food Hypersensitivity; D006801:Humans; D008297:Male; D008892:Milk / Q000276:immunology; D008954:Models, Biological; D010063:Ovum / Q000276:immunology; D011237:Predictive Value of Tests; D012189:Retrospective Studies